Unified deep neural network model for acoustic echo cancellation and residual echo suppression

ABSTRACT

A method, computer program, and computer system is provided for an all-deep-learning based AEC system by recurrent neural networks. The model consists of two stages, echo estimation stage and echo suppression stage, respectively. Two different schemes for echo estimation are presented herein: linear echo estimation by multi-tap filtering on far-end reference signal and non-linear echo estimation by single-tap masking on microphone signal. A microphone signal waveform and a far-end reference signal waveform are received. An echo signal waveform is estimated based on the microphone signal waveform and a far-end reference signal waveform. A near-end speech signal waveform is output based on subtracting the estimated echo signal waveform from the microphone signal waveform, and echoes are suppressed within the near-end speech signal waveform.

FIELD

This disclosure relates generally to field of data processing, and moreparticularly to neural networks.

BACKGROUND

Acoustic echo cancellation (AEC) plays an important role in thefull-duplex speech communication as well as the front-end speechenhancement for recognition in the conditions when the loudspeaker playsback.

SUMMARY

Embodiments relate to a method, system, and computer readable medium foracoustic echo suppression. According to one aspect, a method foracoustic echo suppression is provided. The method may include receivinga microphone signal waveform and a far-end reference signal waveform. Anecho signal waveform is estimated based on the microphone signalwaveform and a far-end reference signal waveform. A near-end speechsignal waveform is output based on subtracting the estimated echo signalwaveform from the microphone signal waveform.

According to another aspect, a computer system for acoustic echosuppression is provided. The computer system may include one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include receiving a microphonesignal waveform and a far-end reference signal waveform. An echo signalwaveform is estimated based on the microphone signal waveform and afar-end reference signal waveform. A near-end speech signal waveform isoutput based on subtracting the estimated echo signal waveform from themicrophone signal waveform.

According to yet another aspect, a computer readable medium for acousticecho suppression is provided. The computer readable medium may includeone or more computer-readable storage devices and program instructionsstored on at least one of the one or more tangible storage devices, theprogram instructions executable by a processor. The program instructionsare executable by a processor for performing a method that mayaccordingly include receiving a microphone signal waveform and a far-endreference signal waveform. An echo signal waveform is estimated based onthe microphone signal waveform and a far-end reference signal waveform.A near-end speech signal waveform is output based on subtracting theestimated echo signal waveform from the microphone signal waveform.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIGS. 2A and 2B are diagrams of echo suppression systems, according toembodiments;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program that suppresses residual acoustic echoes, according to atleast one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1 , according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5 , according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of data processing, and moreparticularly to neural network. The following described exemplaryembodiments provide a system, method and computer program to, amongother things, acoustic echo suppression. Therefore, some embodimentshave the capacity to improve the field of computing by allowing for acomputer to suppress residual acoustic echoes in full-duplex speechcommunication.

As previously described, acoustic echo cancellation (AEC) plays animportant role in the full-duplex speech communication as well as thefront-end speech enhancement for recognition in the conditions when theloudspeaker plays back.

However, acoustic echoes caused by the sound from the loudspeaker whichis received by the near-end microphone and then transmitted to thefar-end listener or speech recognition engine. Such interfering signaldegrades the voice quality in teleconference system, mobilecommunication and hand-free human-machine interaction. Adaptivefiltering methods have been studied over decades. Normalized least meansquare (NLMS) is most widely used due to its robustness and lowcomplexity, such as frequency domain block adaptive filter (FDBAF) andthe multi-delay block frequency domain (MDF) adaptive filter. Thenon-linear post processing is usually cascaded for residual echosuppression (RES). Instead of the conventional post-processing, deeplearning methods have been used for RES purpose. Therefore linearadaptive filtering followed by the neural network based RES is adoptedfor AEC system design.

It may be advantageous, therefore, to use an end-to-end model for AEC.Both the echo estimation for linear echo cancellation (stage 1) andresidual echo suppression (stage 2) are presented in a unified model.The recurrent neural network (RNN) is used for the learning oftime-variant multi-tap linear filters in stage 1 as well as thesingle-tap RES mask in stage 2. The echo estimation, 1^(st) stage echocancellation, and 2^(nd) stage echo suppression are all modeled in theunified model. The multi-tap linear filtering on the far-end referencesignal performs in a similar way as the adaptive linear filtering basedAEC. For handling large loudspeaker distortion, a single-tap maskingbased non-linear echo estimation directly on the microphone signal isinvented instead. The masking based echo estimation on the microphonesignal captures the non-linearity of the loudspeaker and reverberationof the acoustic echo path.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that uses an all-deep-learning based AEC system byrecurrent neural networks. The model consists of two stages, echoestimation stage and echo suppression stage, respectively. Two differentschemes for echo estimation are presented herein: linear echo estimationby multi-tap filtering on far-end reference signal and non-linear echoestimation by single-tap masking on microphone signal.

Referring now to FIG. 1 , a functional block diagram of a networkedcomputer environment illustrating an acoustic echo cancellation system100 (hereinafter “system”) for residual echo suppression. It should beappreciated that FIG. 1 provides only an illustration of oneimplementation and does not imply any limitations with regard to theenvironments in which different embodiments may be implemented. Manymodifications to the depicted environments may be made based on designand implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (laaS), as discussed below withrespect to FIGS. 5 and 6 . The server computer 114 may also be locatedin a cloud computing deployment model, such as a private cloud,community cloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for acoustic echo suppressionis enabled to run an Acoustic Echo Suppression Program 116 (hereinafter“program”) that may interact with a database 112. The Acoustic EchoSuppression Program method is explained in more detail below withrespect to FIG. 3 . In one embodiment, the computer 102 may operate asan input device including a user interface while the program 116 may runprimarily on server computer 114. In an alternative embodiment, theprogram 116 may run primarily on one or more computers 102 while theserver computer 114 may be used for processing and storage of data usedby the program 116. It should be noted that the program 116 may be astandalone program or may be integrated into a larger acoustic echosuppression program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 maybe implemented within a single device, or a single device shown in FIG.1 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring to FIGS. 2A and 2B, respective multi-stage echo suppressionsystems 200A and 200B are depicted. The echo suppression system 200A ofFIG. 2A suppresses echoes based on the echo estimation in a first stagebeing passed through a multi-tap filtering of far-end reference signal.The echo suppression system 200B of FIG. 2B suppresses echoes based onthe echo estimation in the first stage being passed through a single-tapmasking of a microphone signal.

The echo suppression system 200A, 200B takes two inputs: (i) microphonesignal waveforms, (ii) far-end reference signal waveforms. In the firststage, the system estimates the acoustic echo signal. By subtracting theestimated echo signal from the microphone signal, the resulted echocancelled signal is then passed to the second stage together with themicrophone signal and estimated echo signal. Served as the residual echosuppression, the second stage of the model estimates the near-end speechsignal. Two loss functions are applied to the first stage echoestimation and second stage near-end signal estimation, respectively. Inboth, scale-invariant signal to distortion ratio (SI-SDR) on time domainsignals and L1 loss on time-frequency spectral magnitude are sum overtogether as the training objective. The type-1 model echo suppressionsystem 200A of FIG. 2A computes the echo signal by linear filtering onthe far-end signal, while the type-2 model echo suppression system 200Bof FIG. 2B computes the echo signal by multiplying a single-tap mask tothe microphone signal spectrum. Thus, the all-deep-learning-basedtwo-stage unified acoustic echo cancellation model disclosed herein aimsto solve the problems of echo estimation, acoustic echo cancellation andresidual echo suppression.

Referring now to FIG. 3 , an operational flowchart illustrating thesteps of a method 300 carried out by a program that acoustic echocancellation is depicted.

At 302, the method 300 may include receiving a microphone signalwaveform and a far-end reference signal waveform.

At 304, the method 300 may include estimating an echo signal waveformbased on the microphone signal waveform and a far-end reference signalwaveform.

At 306, the method 300 may include estimating a near-end speech signalwaveform based on subtracting the estimated echo signal waveform fromthe microphone signal waveform. The near-end speech signal waveform maybe output such that echoes may be suppressed.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1 ) and server computer 114 (FIG. 1 ) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5 . Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1) and the Acoustic Echo Suppression Program 116 (FIG. 1 ) on servercomputer 114 (FIG. 1 ) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMs 822 (which typically include cache memory). In the embodimentillustrated in FIG. 4 , each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1 ) and the Acoustic Echo Suppression Program 116(FIG. 1 ) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1 ) and theAcoustic Echo Suppression Program 116 (FIG. 1 ) on the server computer114 (FIG. 1 ) can be downloaded to the computer 102 (FIG. 1 ) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the Acoustic EchoSuppression Program 116 on the server computer 114 are loaded into therespective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (laaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5 , illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6 , a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Acoustic Echo Suppression 96. AcousticEcho Suppression 96 may suppress residual acoustic echoes in full-duplexcommunication.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of acoustic echo suppression, executableby a processor, comprising: receiving a microphone signal waveform and afar-end reference signal waveform; estimating an echo signal waveformbased on the microphone signal waveform and a far-end reference signalwaveform; and outputting a near-end speech signal waveform based onsubtracting the estimated echo signal waveform from the microphonesignal waveform, wherein echoes are suppressed within the near-endspeech signal waveform.
 2. The method of claim 1, wherein the echosignal is computed based on linear filtering on the far-end referencesignal waveform.
 3. The method of claim 1, wherein the echo signal iscomputed based on multiplying a single-tap mask by a signal spectrumassociated with the microphone signal waveform.
 4. The method of claim1, wherein the echo signal is estimated by a first stage and thenear-end speech signal is estimated by a second stage.
 5. The method ofclaim 4, wherein the first stage applies a scale-invariant signal todistortion ratio on time domain signals associated with the microphonesignal waveform and the far-end reference signal waveform and a loss ona time-frequency spectral magnitude associated with the microphonesignal waveform and the far-end reference signal waveform.
 6. The methodof claim 4, wherein the second stage applies a scale-invariant signal todistortion ratio on time domain signals associated with the microphonesignal waveform and the echo signal waveform and a loss on atime-frequency spectral magnitude associated with the microphone signalwaveform and the echo signal waveform.
 7. The method of claim 4, whereinthe first stage and the second stage comprise a recurrent neuralnetwork.
 8. A computer system for acoustic echo suppression, thecomputer system comprising: one or more computer-readable non-transitorystorage media configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: receiving code configured to cause the one ormore computer processors to receive a microphone signal waveform and afar-end reference signal waveform; estimating code configured to causethe one or more computer processors to estimate an echo signal waveformbased on the microphone signal waveform and a far-end reference signalwaveform; and outputting estimating code configured to cause the one ormore computer processors to output a near-end speech signal waveformbased on subtracting the estimated echo signal waveform from themicrophone signal waveform, wherein echoes are suppressed within thenear-end speech signal waveform.
 9. The computer system of claim 8,wherein the echo signal is computed based on linear filtering on thefar-end reference signal waveform.
 10. The computer system of claim 8,wherein the echo signal is computed based on multiplying a single-tapmask by a signal spectrum associated with the microphone signalwaveform.
 11. The computer system of claim 8, wherein the echo signal isestimated by a first stage and the near-end speech signal is estimatedby a second stage.
 12. The computer system of claim 11, wherein thefirst stage applies a scale-invariant signal to distortion ratio on timedomain signals associated with the microphone signal waveform and thefar-end reference signal waveform and a loss on a time-frequencyspectral magnitude associated with the microphone signal waveform andthe far-end reference signal waveform.
 13. The computer system of claim11, wherein the second stage applies a scale-invariant signal todistortion ratio on time domain signals associated with the microphonesignal waveform and the echo signal waveform and a loss on atime-frequency spectral magnitude associated with the microphone signalwaveform and the echo signal waveform.
 14. The computer system of claim11, wherein the first stage and the second stage comprise a recurrentneural network.
 15. A non-transitory computer readable medium havingstored thereon a computer program for acoustic echo suppression, thecomputer program configured to cause one or more computer processors to:receive a microphone signal waveform and a far-end reference signalwaveform; estimate an echo signal waveform based on the microphonesignal waveform and a far-end reference signal waveform; and output anear-end speech signal waveform based on subtracting the estimated echosignal waveform from the microphone signal waveform, wherein echoes aresuppressed within the near-end speech signal waveform.
 16. The computerreadable medium of claim 15, wherein the echo signal is computed basedon linear filtering on the far-end reference signal waveform.
 17. Thecomputer readable medium of claim 15, wherein the echo signal iscomputed based on multiplying a single-tap mask by a signal spectrumassociated with the microphone signal waveform.
 18. The computerreadable medium of claim 15, wherein the echo signal is estimated by afirst stage and the near-end speech signal is estimated by a secondstage.
 19. The computer readable medium of claim 18, wherein the firststage applies a scale-invariant signal to distortion ratio on timedomain signals associated with the microphone signal waveform and thefar-end reference signal waveform and a loss on a time-frequencyspectral magnitude associated with the microphone signal waveform andthe far-end reference signal waveform.
 20. The computer readable mediumof claim 18, wherein the second stage applies a scale-invariant signalto distortion ratio on time domain signals associated with themicrophone signal waveform and the echo signal waveform and a loss on atime-frequency spectral magnitude associated with the microphone signalwaveform and the echo signal waveform.